Solving the short run economic dispatch problem using concurrent constraint programming

This paper shows a description of an application for solving the Short-Run Economic Dispatch Problem. This problem consists of searching the active power hourly schedule generated in electrical networks in order to meet the demand at minimum cost. The solution cost is associatted to the inmediate costs of thermal units and the future costs of hydropower stations. The application was implemented using Mozart with real-domain constraints and a hybrid model among real (XRI) and finite domains (FD). The implemented tool showed promising results since the found solution costs were lower than those found in the literature for the same kind of problems. On the other hand, in order to test the tool against real problems, a system with data from real networks was implemented and the solution found was good enough in terms of time efficiency and accuracy. Also, this paper shows the usability of Mozart language to model real combinatory problems.Applications in Artificial Intelligence ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Solving the short run economic dispatch problem using concurrent constraint programming

This paper shows a description of an application for solving the Short-Run Economic Dispatch Problem. This problem consists of searching the active power hourly schedule generated in electrical networks in order to meet the demand at minimum cost. The solution cost is associatted to the inmediate costs of thermal units and the future costs of hydropower stations. The application was implemented using Mozart with real-domain constraints and a hybrid model among real (XRI) and finite domains (FD). The implemented tool showed promising results since the found solution costs were lower than those found in the literature for the same kind of problems. On the other hand, in order to test the tool against real problems, a system with data from real networks was implemented and the solution found was good enough in terms of time efficiency and accuracy. Also, this paper shows the usability of Mozart language to model real combinatory problems.Applications in Artificial Intelligence ApplicationsRed de Universidades con Carreras en Informática (RedUNCI

Short Term Unit Commitment as a Planning Problem

‘Unit Commitment’, setting online schedules for generating units in a power system to ensure supply meets demand, is integral to the secure, efficient, and economic daily operation of a power system. Conflicting desires for security of supply at minimum cost complicate this. Sustained research has produced methodologies within a guaranteed bound of optimality, given sufficient computing time. Regulatory requirements to reduce emissions in modern power systems have necessitated increased renewable generation, whose output cannot be directly controlled, increasing complex uncertainties. Traditional methods are thus less efficient, generating more costly schedules or requiring impractical increases in solution time. Meta-Heuristic approaches are studied to identify why this large body of work has had little industrial impact despite continued academic interest over many years. A discussion of lessons learned is given, and should be of interest to researchers presenting new Unit Commitment approaches, such as a Planning implementation. Automated Planning is a sub-field of Artificial Intelligence, where a timestamped sequence of predefined actions manipulating a system towards a goal configuration is sought. This differs from previous Unit Commitment formulations found in the literature. There are fewer times when a unit’s online status switches, representing a Planning action, than free variables in a traditional formulation. Efficient reasoning about these actions could reduce solution time, enabling Planning to tackle Unit Commitment problems with high levels of renewable generation. Existing Planning formulations for Unit Commitment have not been found. A successful formulation enumerating open challenges would constitute a good benchmark problem for the field. Thus, two models are presented. The first demonstrates the approach’s strength in temporal reasoning over numeric optimisation. The second balances this but current algorithms cannot handle it. Extensions to an existing algorithm are proposed alongside a discussion of immediate challenges and possible solutions. This is intended to form a base from which a successful methodology can be developed

Designing a Software Platform for Evaluating Cyber-Attacks on The Electric PowerGrid

abstract: Energy management system (EMS) is at the heart of the operation and control of a modern electrical grid. Because of economic, safety, and security reasons, access to industrial grade EMS and real-world power system data is extremely limited. Therefore, the ability to simulate an EMS is invaluable in researching the EMS in normal and anomalous operating conditions. I first lay the groundwork for a basic EMS loop simulation in modern power grids and review a class of cybersecurity threats called false data injection (FDI) attacks. Then I propose a software architecture as the basis of software simulation of the EMS loop and explain an actual software platform built using the proposed architecture. I also explain in detail the power analysis libraries used for building the platform with examples and illustrations from the implemented application. Finally, I will use the platform to simulate FDI attacks on two synthetic power system test cases and analyze and visualize the consequences using the capabilities built into the platform.Dissertation/ThesisMasters Thesis Computer Science 201

Distribution network optimisation for an active network management system

The connection of Distributed Generators (DGs) to a distribution network causes technical concerns for Distribution Network Operators (DNOs) which include power flow management, loss increase and voltage management problems. An Active Network Management System can provide monitoring and control of the distribution network as well as providing the infrastructure and technology for full integration of DGs into the distribution network. The Optimal Power Flow (OPF) method is a valuable tool in providing optimal control solutions for active network management system applications. The research presented here has concentrated on the development of a multi-objective OPF to provide power flow management, voltage control solutions and network optimisation strategies. The OPF has been shown to provide accurate solutions for variety of network topologies. It is possible to apply time-series of load and generation data to the OPF in a loop, generating optimal network solutions to maintain the network within thermal and voltage limits. The OPF incorporates not only the DG real power output maximisation, but also network loss minimisation as well as minimising the dispatch of DG reactive power. This investigation uses a direct Interior Point (IP) method as the solution methodology which is speed efficient and converges in polynomial time. Each objective function has been assigned a weighting factor, making it possible to favour one objective function and ignore the others. Contributions to enhance the performance of the IP OPF algorithm include a new generic barrier parameter formulation and a new swing bus formulation to model energy export/import in the main optimisation routine. A Terminal Voltage Regulator Mode (TVRM) and Power Factor Regulation Mode (PFRM) for DG were incorporated in the main optimisation routine. The main motivation is to compare these two decentralised DG control methods in terms of the achieving the maximum DG real power generation. The DG operation methods of TVRM and PFRM are compared with the optimisation results obtained from centralised dispatch in terms of the DG capacity achieved as it produces the optimum overall network solution. A suitable value of the droop and local voltage regulator dead-bands were determined for particular DGs. Furthermore, the effect of these decentralised DG control methods on distribution network losses are considered in a measure to assess the financial implications from a DNO's perspective

Optimization Models and Algorithms for Vulnerability Analysis and Mitigation Planning of Pyro-Terrorism

In this dissertation, an important homeland security problem is studied. With the focus on wildfire and pyro-terrorism management. We begin the dissertation by studying the vulnerability of landscapes to pyro-terrorism. We develop a maximal covering based optimization model to investigate the impact of a pyro-terror attack on landscapes based on the ignition locations of fires. We use three test case landscapes for experimentation. We compare the impact of a pyro-terror wildfire with the impacts of naturally-caused wildfires with randomly located ignition points. Our results indicate that a pyro-terror attack, on average, has more than twice the impact on landscapes than wildfires with randomly located ignition points. In the next chapter, we develop a Stackelberg game model, a min-max network interdiction framework that identifies a fuel management schedule that, with limited budget, maximally mitigates the impact of a pyro-terror attack. We develop a decomposition algorithm called MinMaxDA to solve the model for three test case landscapes, located in Western U.S. Our results indicate that fuel management, even when conducted on a small scale (when 2% of a landscape is treated), can mitigate a pyro-terror attack by 14%, on average, comparing to doing nothing. For a fuel management plan with 5%, and 10% budget, it can reduce the damage by 27% and 43% on average. Finally, we extend our study to the problem of suppression response after a pyro-terror attack. We develop a max-min model to identify the vulnerability of initial attack resources when used to fight a pyro-terror attack. We use a test case landscape for experimentation and develop a decomposition algorithm called Bounded Decomposition Algorithm (BDA) to solve the problem since the model has bilevel max-min structure with binary variables in the lower level and therefore not solvable by conventional methods. Our results indicate that although pyro-terror attacks with one ignition point can be controlled with an initial attack, pyro-terror attacks with two and more ignition points may not be controlled by initial attack. Also, a faster response is more promising in controlling pyro-terror fires

Heuristics for Lagrangian Relaxation Formulations for the Unit Commitment Problem

The expansion of distributed energy resources (DER), demand response (DR), and virtual bidding in many power systems and energy markets are creating new challenges for unit commitment (UC) and economic dispatch (ED) techniques. Instead of a small number of traditionally large generators, the power system resource mix is moving to one with a high percentage of a large number of small units. These can increase the number of similar or identical units, leading to chattering (switching back and forth among committed units between iterations). This research investigates alternative and scalable ways of increasing the high penetration of these resources. First, the mathematical formulations for UC and ED models are reviewed. Then a new heuristic is proposed that takes advantage of the incremental nature of Lagrangian relaxation (LR). The heuristic linearizes and distributes the network transmission losses to appropriately penalize line flow and mitigate losses. Second, a mixed integer programming (MIP) is used as a benchmark for the proposed LR formulation. The impact of similar and identical units on the solution quality and simulation run time of UC and ED was investigated using the proposed formulation. Third, a system flexibility study is done using DR and a load demand pattern with a high penetration of renewables, creating a high daily ramp rate requirement. This work investigates the impact of available DR on spikes in locational marginal pricing (LMP). Fourth, two studies are done on improving LR computational efficiency. The first proposes a heuristic that focuses on trade-offs between solution quality and simulation run time. The heuristic iterates over lambda and energy marginal price while the convergence issue is handled using Augmented LR (ALR). The second study proposes a heuristic that penalizes transmission lines with binding line limits. The proposed method can reduce power flow in the transmission lines of interest, and considerably reduce the simulation time in optimization problems with a high number of transmission constraints. Finally, the effect of a large number of similar and identical units on simulation run time is considered. The proposed formulation scales linearly with the increase in system size